class_docstr: Binary cross-entropy discriminator loss function.
  
  This callable applies the standard non-saturating binary cross-entropy
  loss for training GANs.
  
  Args:
      real_logits: Logits of the real input.
      fake_logits: Logits of the fake input.
      target: The target for crossentropy. Typically, this will be a ones
          tensor of the same size as ``real_logits``.
  
  Returns:
      The binary cross-entropy discriminator loss.
code: def __call__(
      self, real_logits: Tensor, fake_logits: Tensor, target: Tensor
  ) -> Tensor:
      if target.ndim == 0:
          target = target * torch.ones_like(real_logits)
      elif target.ndim != real_logits.ndim:
          raise ValueError("Misconfigured GAN loss starget dimension.")
  
      return 0.5 * (
          F.binary_cross_entropy_with_logits(real_logits, target)
          + F.binary_cross_entropy_with_logits(
              fake_logits,
              torch.zeros_like(target),
          )
      )
  
file_path: NeuralCompression/neuralcompression/loss_fn/_binary_crossentropy_loss.py
imports: import torch
  import torch.nn.functional
  from torch import Tensor
  from ._gan_losses import DiscriminatorLoss, GeneratorLoss
label_desc: Use image node_id 1 for calling the BinaryCrossentropyDiscriminatorLoss obj's underlying member method code with example usage: obj.__call__(real_logits, fake_logits, target) and returns: unknown